Your work will support the transition of cutting-edge research into fielded capabilities, helping Shield AI deliver precision navigation solutions for mission-critical applications.

Responsibilities:

• Develop and implement real-time state estimation algorithms including inertial navigation, sensor fusion, and alternative navigation methods for GPS-denied or degraded environments.

• Integrate data from IMUs, GNSS receivers, visual odometry, magnetometers, barometers, and radar into robust estimation frameworks.

• Design sensor processing pipelines focused on accuracy, robustness, and system-level fault tolerance.

• Collaborate with autonomy, software, and hardware teams to ensure end-to-end integration of navigation and PNT systems.

• Conduct simulation, lab testing, and field trials to evaluate algorithm performance under real-world conditions.

• Stay current on advancements in state estimation and navigation technologies and help adapt new innovations into deployable solutions.

Qualifications:

• Typically requires a minimum of 3 years of relevant experience with a bachelor’s degree; or 2 years with a master’s degree; or 1 years with a PhD; or equivalent practical experience.

• Familiarity with algorithms.

• Proficient in C++11 or newer in real-time environments.

• Comfortable working in Linux, with experience using standard command-line tools and scripting.

• Strong written and verbal communication skills with a collaborative mindset.

• Demonstrated success working in fast-paced development cycles and delivering high-quality results.

Preferred Qualifications:

• Experience developing and deploying real-time navigation or sensor fusion algorithms using IMUs, GPS, or other sensors.

• Strong understanding of filtering and estimation techniques (e.g., Kalman filters, EKF, UKF, particle filters).

• Experience implementing inertial navigation algorithms in degraded or GPS-denied conditions.

• Exposure to visual odometry or computer vision-based navigation approaches.

• Experience optimising code for performance on compute-constrained platforms.

• Familiarity with CUDA or hardware acceleration techniques (e.g., FPGAs).

• Experience transitioning navigation solutions from research into production environments.

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Your work will support the transition of cutting-edge research into fielded capabilities, helping Shield AI deliver precision navigation solutions for mission-critical applications.

Responsibilities:

• Develop and implement real-time state estimation algorithms including inertial navigation, sensor fusion, and alternative navigation methods for GPS-denied or degraded environments.
• Integrate data from IMUs, GNSS receivers, visual odometry, magnetometers, barometers, and radar into robust estimation frameworks.
• Design sensor processing pipelines focused on accuracy, robustness, and system-level fault tolerance.
• Collaborate with autonomy, software, and hardware teams to ensure end-to-end integration of navigation and PNT systems.
• Conduct simulation, lab testing, and field trials to evaluate algorithm performance under real-world conditions.
• Stay current on advancements in state estimation and navigation technologies and help adapt new innovations into deployable solutions.

Qualifications:

• Typically requires a minimum of 3 years of relevant experience with a bachelor’s degree; or 2 years with a master’s degree; or 1 years with a PhD; or equivalent practical experience.
• Familiarity with algorithms.
• Proficient in C++11 or newer in real-time environments.
• Comfortable working in Linux, with experience using standard command-line tools and scripting.
• Strong written and verbal communication skills with a collaborative mindset.
• Demonstrated success working in fast-paced development cycles and delivering high-quality results.

Preferred Qualifications:

• Experience developing and deploying real-time navigation or sensor fusion algorithms using IMUs, GPS, or other sensors.
• Strong understanding of filtering and estimation techniques (e.g., Kalman filters, EKF, UKF, particle filters).
• Experience implementing inertial navigation algorithms in degraded or GPS-denied conditions.
• Exposure to visual odometry or computer vision-based navigation approaches.
• Experience optimizing code for performance on compute-constrained platforms.
• Familiarity with CUDA or hardware acceleration techniques (e.g., FPGAs).
• Experience transitioning navigation solutions from research into production environments.

XML job scraping automation by YubHub